Efficient Learning of Linear Predictors using Dimensionality Reduction (Oral)

Using Linear Predictors for template tracking enables fast and reliable, real-time processing. However, not being able to learn new templates online limits their use in applications where the scene is not known a priori and multiple templates have to be added online, such as SLAM or SfM. This especially holds for applications running on low-end hardware such as mobile devices. For previous approaches, Linear Predictors had to be either learned offine [1] or by starting with a small template and iteratively growing it over time [2]. We propose a fast and simple learning procedure which reduces the necessary training time by up to two orders of magnitude while also slightly improving the tracking robustness with respect to large motions and image noise. This is demonstrated in an exhaustive evaluation where we compare our approach with state-of-the-art. Additionally, we show the learning and tracking in mobile phone applications which demonstrates the effciency of the proposed approach.     «

Using Linear Predictors for template tracking enables fast and reliable, real-time processing. However, not being able to learn new templates online limits their use in applications where the scene is not known a priori and multiple templates have to be added online, such as SLAM or SfM. This especially holds for applications running on low-end hardware such as mobile devices. For previous approaches, Linear Predictors had to be either learned offine [1] or by starting with a small template and...     »